20170602更新说明：
- 原项目很久没更新了，修复以用以python3项目。
- 先安装：
pip3 install jieba - 安装：
python3 setup.py install
如果在容器内安装，可能会出现错误：sh: 1: make: not found, 这时运行可能会报错OSError: /usr/local/lib/python3.4/dist-packages/tgrocery/learner/util.so.1: cannot open shared object file: No such file or directory：
解决：可以在外部编译一次，然后挂载到容器内部再编译一次。
A simple, efficient short-text classification tool based on LibLinear
Embed with jieba as default tokenizer to support Chinese tokenize
Other languages: 更详细的中文文档
- Train set: 48k news titles with 32 labels
- Test set: 16k news titles with 32 labels
- Compare with svm and naive-bayes of scikit-learn
| Classifier | Accuracy | Time cost(s) |
|---|---|---|
| scikit-learn(nb) | 76.8% | 134 |
| scikit-learn(svm) | 76.9% | 121 |
| TextGrocery | 79.6% | 49 |
>>> from tgrocery import Grocery
# Create a grocery(don't forget to set a name)
>>> grocery = Grocery('sample')
# Train from list
>>> train_src = [
('education', 'Student debt to cost Britain billions within decades'),
('education', 'Chinese education for TV experiment'),
('sports', 'Middle East and Asia boost investment in top level sports'),
('sports', 'Summit Series look launches HBO Canada sports doc series: Mudhar')
]
>>> grocery.train(train_src)
# Or train from file
>>> grocery.train('train_ch.txt')
# Save model
>>> grocery.save()
# Load model(the same name as previous)
>>> new_grocery = Grocery('sample')
>>> new_grocery.load()
# Predict
>>> new_grocery.predict('Abbott government spends $8 million on higher education media blitz')
education
# Test from list
>>> test_src = [
('education', 'Abbott government spends $8 million on higher education media blitz'),
('sports', 'Middle East and Asia boost investment in top level sports'),
]
>>> new_grocery.test(test_src)
# Return Accuracy
1.0
# Or test from file
>>> new_grocery.test('test_ch.txt')
# Custom tokenize
>>> custom_grocery = Grocery('custom', custom_tokenize=list)More examples: sample/
$ pip install tgrocery
Only test under Unix-based System